Skip to main content
NCBI home page
Skin Health and Disease logoLink to Skin Health and Disease
. 2026 Jul 10;6(5):680–689. doi: 10.1093/skinhd/vzag069

Prevalence of vitiligo in East Africa: systematic review and meta-analysis

Kedir Seid 1,✉, Chala Getaneh Jaleta 2, Sintayehu Samuel Lorato 3, Mitiku Desalegn 4, Dawit Tesfaye Rundasa 5, Yohannes Godie Ashebir 6, Olyad Kuma Getahun 7, Gebeyehu Lakew 8, Mathewos Mekonnen Gemmechu 9, Mulualem Endeshaw Zeleke 10, Yibeltal Assefa Atalay 11, Amlaku Nigusie Yirsaw 12, Eyob Ketema Bogale 13, Abiy Hailu Tikuneh 14, Abas Ali Hassen 15, Eyasu Alem Lake 16, Tadesse Nigussie 17, Bushra Bibi 18, Fikiru Yigezu Jaleta 19, Abebe Gedefaw Belete 20, Genanew Kassie Getahun 21
PMCID: PMC13623449  PMID: 42813184

Abstract

Background

Vitiligo is a chronic skin disorder characterized by the loss of melanocytes, resulting in depigmented macules and patches. No accurate estimate has been made of the prevalence of vitiligo in East Africa.

Objectives

To determine the prevalence of vitiligo in East Africa via a systematic review and meta-analysis of the literature.

Methods

A systematic review and meta-analysis were performed in accordance with the PRISMA guidelines. Eleven articles were retrieved from PubMed/MEDLINE, Embase, Scopus, Google Scholar, Science Direct, Web of Science and African Journals Online. Microsoft Excel was used to extract the data, which were then exported to STATA 17 for analysis using a random effects model. Heterogeneity of studies was assessed by Cochran’s Q test and I2 statistics. Publication bias was detected with a funnel plot and the Egger test.

Results

The 11 studies retrieved from the literature search provided a total sample size of 8444 individuals reported between 2000 and 2025. The overall pooled prevalence of vitiligo was 6% [95% confidence interval (CI) 3–8], while the prevalence among male and female individuals was 54% (95% CI 19–73) and 46% (95% CI 3–61), respectively. The prevalence in children was 3% (95% CI 2–5), 9% (95% CI 7–12) in adults and 7% (95% CI 2–11) in the general population. The models showed that the prevalence of vitiligo in Tanzania was 1% (95% CI 0–3), while the pooled prevalence of vitiligo in Ethiopia was 8% (95% CI 5–11).

Conclusions

In East Africa, the combined prevalence of vitiligo was relatively high. The increased prevalence of vitiligo highlights the need for health policymakers to pay attention to vitiligo.

Vitiligo is an important dermatological condition affecting populations worldwide, yet its burden in East Africa remains poorly understood. This systematic review and meta-analysis reveals a pooled prevalence of 6%, highlighting the need for increased public health attention and research in the region.

Vitiligo is an acquired, idiopathic and prevalent skin depigmentation condition. Milky white (depigmented) patches on the skin are the hallmark of vitiligo, caused by a lack of functioning melanocytes. It can present at any age and affects both sexes equally. Additionally, it can cause hair whitening.1,2 This chronic condition, which primarily affects the hands and face, causes white spots to form on the skin. Physical symptoms like itching are rare. Vitiligo often first appears in childhood or early adulthood, typically between the age of 10 and 30 years; however, it can occur at any age and tends to recur in spring and summer.3–6 People of all races can be affected by vitiligo; however, it can be more obvious in people with more richly pigmented skin.7 Owing to the selective loss of melanocytes, vitiligo is characterized clinically by ­pale-to-milky white macules or patches. They can appear on the skin in various body areas and, occasionally, on the mucous membranes.

The pathogenesis of vitiligo may involve a variety of processes, such as oxidative stress, cell detachment, inflammatory mediator production, metabolic problems and immunological reactions. In particular, an autoimmune mechanism is now well recognized. The cause of the condition is still under debate.8 Patients with vitiligo have a higher chance of developing mental illnesses, especially anxiety and depression, which could be exacerbated by their skin condition or the psychological effects of social shame.9 It is linked to thyroid disorders, systemic lupus erythematosus, alopecia areata and pernicious anaemia, among other autoimmune conditions.10

Reactive oxygen species, produced by a number of sources and indicative of a disturbance in the melanocyte redox balance and compromised antioxidant defences, may trigger the autoimmune reactions that result in vitiligo. Melanocytes under this oxidative stress generate damage-associated molecular patterns or autoantigens, which trigger innate and adaptive immunological responses. Thus, melanocyte malfunction and, ultimately, cell death are the outcomes of this inflammatory cascade.7 Vitiligo can be significantly influenced by the levels of certain vitamins and minerals, including copper, magnesium and vitamin D.11

The physical manifestation of vitiligo can have a major effect on patients’ mental health. Depending on its extent and distribution, vitiligo is classified as either localized or generalized. Symmetric depigmented patches on the body are a hallmark of generalized vitiligo. Segmental vitiligo is less common and manifests as asymmetrical, localized or dermatomal depigmented patches or macules.12 Although any part of the body may be affected, lesions typically develop in locations that are exposed to the sun, areas that are hyperpigmented or in places of pressure and stretch, such as the face, dorsum of the hands and fingers, external genitalia, knees and elbows.13

Prevalence estimates of vitiligo vary greatly worldwide, ranging from 0.004% to 2.28%.13 Vitiligo affects 3% of people in Africa and 2% of people in Arabian countries. In contrast, the frequency is 1% in both sexes, 2% in children and 10% in adults.14

Vitiligo is difficult to treat. Treatment is expensive and frequently has disappointing results. In numerous investigations, the threshold for success has been arbitrarily placed at 50–75% repigmentation. The patient’s skin condition and triggers, such as stress levels, have an impact on the prognosis of vitiligo. Unaware of this fact, patients may abruptly stop treatment and seek advice elsewhere, which may result in noncompliance.12,15,16

In addition to its effects on a person’s appearance, vitiligo can have psychological impacts. These impacts might range from moderate humiliation to a serious loss of confidence and social anxiety, particularly in those whose skin lesions are visible to others.17,18 Individuals with vitiligo may undergo significant lifestyle changes as a result of societal stigmatization and discrimination. This can involve avoiding social events and outdoor activities, as well as dressing differently, applying sunscreen and cosmetically concealing lesions.19–21

Owing to the dearth of information regarding the risk factors for and prevalence of vitiligo in East Africa, we performed a systematic review and meta-analysis to evaluate the pooled prevalence of the condition in this region.

Materials and methods

Protocol and registration

The PRISMA guidelines were followed for the reporting of this systematic review.22

Search strategy

We conducted a search of PubMed/MEDLINE, Embase, Scopus, Google Scholar, Science Direct, Web of Science and African Journals Online to identify all relevant papers published between August 2000 and August 2025. Moreover, we used pubmed.ncbi.nlm.nih.gov general internet search engines (http://www.google.com and http://www.yahoo.com) to find studies, using the keywords ‘prevalence’, ‘incidence’, ‘vitiligo’, ‘skin’, ‘diseases’, ‘disorders’, ‘conditions’ and ‘population’.

Inclusion and exclusion criteria

All studies that described the prevalence or risk factors of any kind of vitiligo in one or more East African nation was included, regardless of vitiligo type, age group or sex. Exclusion criteria included review articles, editorials, genetic research studies, studies that exclusively included patients with vitiligo and studies not carried out in East Africa.

Data extraction

Two reviewers (K.S. and E.K.B.) extracted the data from the selected articles. All extracted data were arranged as a standardized form that contained the (i) last name of the first author and year of publication; (ii) country in which the study was carried out; (iii) sample size; and (iv) prevalence of vitiligo.

Quality assessment

We evaluated the research technique used in the studies and reporting quality via the Newcastle–Ottawa Scale (NOS).23 The Khambalia and Seen literature quality assessment standards were used to assess the quality of the included studies.24 This is a 9-point scoring system for assessing the risk of bias from participant selection, comparability, and exposure or outcome. The included literature was then ranked and summarized using these criteria. Studies that achieved ≥7 points on the NOS were considered to be of high quality, 4–6 points matched the criteria of ­medium-quality studies and studies receiving <4 points were considered to be of poor quality. Each article was assessed independently by two authors (K.S. and G.L.). All differences of opinion during scoring were resolved through consensus with K.S.

Statistical analysis

To assess the heterogeneity of the prevalence of vitiligo across studies, Cochran’s Q and I2 index were calculated. According to the Q statistic, a P-value <0.05 indicates heterogeneity in the effect size between studies; I2 represents the percentage of total variability due to heterogeneity rather than chance, where a value of 0% indicates no observed heterogeneity and values of 25%, 50% and 75% reflect low, moderate and high heterogeneity, respectively. Owing to the high heterogeneity across studies (I2 > 75%), the overall prevalence was pooled on the basis of random effects (Der Simonian and Laird method) meta-analysis. Forest plots were also generated to illustrate the prevalence with corresponding 95% confidence intervals (CIs) for each study and the overall random effects pooled estimate. The data were analysed with Stata (version 17; StataCorp., Cary, NC, USA); a 95% confidence level was applied to the random effects model.

A subgroup analysis was carried out according to the conditions of the study, country and year of publication. The 95% CIs and pooled odds ratios (ORs) were calculated. To show the number of publications, author names, and individual and pooled ORs with 95% CIs, a forest plot was constructed. Publication bias was assessed by visually inspecting a funnel plot. A P-value <0.05 was considered to indicate statistical significance.25,26

Results

Study selection

Figure 1 illustrates the study selection process. The initial search identified a total of 1121 potentially relevant articles published between August 2000 and August 2025. After the removal of duplicates, and title and abstract screening, 86 full-text articles were assessed for eligibility. Eleven articles that met the inclusion criteria were included in the final meta-analysis (Figure 1).

Figure 1.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

PRISMA flow diagram.

Overview of the included studies

Eleven studies from different East African countries were included in this systematic review and meta-analysis.2,12,27–35 The total number of participants across the 11 studies was 8444. The majority of the studies were of a cross-sectional design (n = 10; 91%).2,12,27,28,30–35 Moreover, four (36%) studies were done in cohorts aged <15 years,27–29,32 one (9%) was carried out in adults only,2 and six (55%) included children and adults.12,30,31,33–35 In addition, five (46%) studies were done in Ethiopia;2,12,28,30,33 three (27%) in Tanzania;27,32,34 one (9%) in Uganda;31 one (9%) in Madagascar;29 and one (9%) in Mozambique.35 All included articles were published in peer-reviewed journals. The prevalence of vitiligo ranged from 0.4% to 13% (Table 1).34,35

Table 1.

Characteristics of the study participants and prevalence of vitiligo in the studies included in the systematic review and meta-analysis (n = 11)

Reference Country Study design Participants Sample size (n) Prevalence of vitiligo (%) Quality score
Komba 201027 Tanzania Cross-sectional Children 420 1.4 7
Mino 20182 Ethiopia Cross-sectional Adults 405 9.4 8
Gashaw 202228 Ethiopia Cross-sectional Children 423 5.7 8
Tsadik 202012 Ethiopia Cross-sectional General population 403 13 9
Mamisoa 201929 Madagascar Retrospective cohort Children 951 13 8
Mamo 202430 Ethiopia Cross-sectional General population 338 5.1 9
Mundeli 202531 Uganda Cross-sectional General population 3444 1.77 8
Kiprono 201532 Tanzania Cross-sectional Children 340 2.9 8
Kibret 202433 Ethiopia Cross-sectional General population 460 7.4 9
Ferié 200634 Tanzania Cross-sectional General population 820 0.4 9
Chhaganlal 200735 Mozambique Cross-sectional General population 780 13 9
Open in a new tab

Pooled prevalence of vitiligo in East Africa

Eleven studies evaluated the prevalence of vitiligo. The model that pooled these studies demonstrated that the overall prevalence of vitiligo in East Africa was 6% (95% CI 3–8); the model had significant heterogeneity (I2 = 98.43%, P = 0.00; Figure 2). Owing to this heterogeneity, a random effects model was used to determine the pooled prevalence of vitiligo. The models that assessed the prevalence of vitiligo across male and female patients included four studies. This model showed that the pooled prevalence in male individuals was 54% (95% CI 19–73); the model had significant heterogeneity (I2 = 99.83%, P = 0.00; Figure 3). However, the model for female patients found that the pooled prevalence was 46% (95% CI 3–61); this model also had significant heterogeneity (I2 = 99.85%, P = 0.00; Figure 4).

Figure 2.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Forest plot of the pooled prevalence of vitiligo in Eastern Africa (n = 11 studies). CI, confidence interval.

Figure 3.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Forest plot of the pooled prevalence of vitiligo in male individuals in Eastern Africa (n = 4 studies). CI, confidence interval.

Figure 4.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Forest plot of the pooled prevalence of vitiligo in female individuals in Eastern Africa (n = 4 studies). CI, confidence interval.

Publication bias

Publication bias was detected with a funnel plot and Egger’s test. Visual inspection of the funnel plot showed slight publication bias among the included studies, as illustrated by the asymmetrical distribution (Figure 5). We performed the traditional version of Egger’s regression-based test (β = 0.00, SE = 0.664, P > 0.99), which found no evidence of publication bias in the included studies.

Figure 5.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Publication bias of the included studies (n = 11). CI, confidence interval.

Subgroup analysis

Subgroup analysis according to year of publication showed that the pooled prevalence of vitiligo was 5% (95% CI −3 to 13) between 2000 and 2010. It decreased to 5% (95% CI 1–9) between 2011 and 2019, after which the prevalence drastically decreased and plateaued at 6% (95% CI 3–10) during 2020–25 (Figure 6).

Figure 6.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Subgroup analysis based on publication year (n = 11 studies). CI, confidence interval.

On subgroup analysis by country, the models showed that the pooled prevalence of vitiligo in Tanzania was 1% (95% CI 0–3), while in Ethiopia it was 8% (95% CI 5–11) (Figure 7).

Figure 7.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Subgroup analysis based on country (n = 11 studies). CI, confidence interval.

In subgroup analysis according to age group, the pooled prevalence of vitiligo in children was 3% (95% CI 2–5), whereas it was 7% (95% CI 2–11) among the general population. Among the adult population, the pooled prevalence of vitiligo was 9% (95% CI 7–12); however, this was based only on the study by Mino and Kelbore (Figure 8).2

Figure 8.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Subgroup analysis based on population group (n = 11 studies). CI, confidence interval.

With regard to study setting, the pooled prevalence of vitiligo from community-based studies was 6% (95% CI 7–10) and 6% (95% CI 2–9) from hospital-based studies (Figure 9).

Figure 9.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Subgroup analysis based on study setting (n = 11 studies). CI, confidence interval.

Discussion

Vitiligo is a prevalent, idiopathic, acquired skin depigmentation condition. The reported frequency of vitiligo varies widely. Therefore, combining the findings of multiple studies into a simple meta-analysis would be useful. To the best of our knowledge, this is the first meta-analysis of the prevalence of vitiligo in East Africa. The meta-analysis comprised a total of 11 studies involving 8444 study participants from 2000 to 2025.

In East Africa, the total pooled prevalence of vitiligo was 6%. This is higher than that reported by other meta-analyses that included articles from around the world (2.28%),36 and from the Middle East and Africa (3%).14 This might be due to differences in living standards, sample size, study area, socioeconomic status and quality of health service provision. However, the results of our meta-analysis are in line with another meta-analysis conducted in Saudi Arabia, which reported a vitiligo prevalence of 7%.37

The present meta-analysis showed that the pooled prevalence among male individuals was 54% compared with 46% in female patients. This is corroborated by earlier research from a community-­based survey in China, which found that the male:­female ratio was 1.6:1, indicating a higher frequency of ­vitiligo in men than in women.37

According to our meta-analysis, the pooled prevalence of vitiligo in Ethiopia is 8% compared with 1% in Tanzania.

We found that the pooled prevalence of vitiligo (9%) is higher in the adult population compared with that of children (3%). Reasons for this may include adults being exposed to the sun for longer durations and experiencing more stress.

Several limitations should be considered with regard to this meta-analysis. The available studies originated from only five countries. The meta-analysis did not include certain patient information, such as site of onset, type of vitiligo and risk factors. These could have a significant impact on the prevalence of vitiligo. The failure to incorporate vitiligo risk variables in this study due to a lack of information in the literature is another potential weakness. Finally, the credibility of our findings may be affected by the substantial variability across several of our models. Despite carrying out multiple subgroup analyses, the level of heterogeneity remained high. This substantial heterogeneity is explained by genetic differences and variations in the characteristics of the populations included in the research.

This meta-analysis and systematic review demonstrates a relatively high prevalence of vitiligo in East Africa. Vitiligo was discovered to be more common among male patients in East Africa. In recent years, the prevalence has remained modest. The findings of this study provide a foundation for additional research. Uncovering the main causes of vitiligo in East Africa should be a focus of future studies.

Supplementary Material

vzag069_Supplementary_Data
vzag069_supplementary_data.docx (24.7KB, docx)

Acknowledgements

We thank the authors of the included studies.

Contributor Information

Kedir Seid, Monitoring and Evaluation Officer, Bati Primary Hospital, Oromia Special Zone, Bati, Ethiopia.

Chala Getaneh Jaleta, Department of Public Health, Menelik II Medical and Health Science College, Addis Ababa, Ethiopia.

Sintayehu Samuel Lorato, Department of Anesthesia, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia.

Mitiku Desalegn, Department of Anesthesia, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia.

Dawit Tesfaye Rundasa, Department of Surgical Nursing, Menelik II Medical and Health Science College, School of Nursing and Midwifery, Addis Ababa, Ethiopia.

Yohannes Godie Ashebir, Department of Neonatal Nursing, Menelik II Medical and Health Science College, Addis Ababa, Ethiopia.

Olyad Kuma Getahun, School of Public Health Menelik II Medical and Health Science College, Addis Ababa, Ethiopia.

Gebeyehu Lakew, Department of Health Promotion and Health Behavior, School of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Mathewos Mekonnen Gemmechu, Department of Pediatric and Child Health Nursing, Menelik II Medical and Health Science College, Addis Ababa, Ethiopia.

Mulualem Endeshaw Zeleke, Department of Anesthesia, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Yibeltal Assefa Atalay, Department of Environmental Health, College of Medicine and Health Science, Injibara University, Injibara, Ethiopia.

Amlaku Nigusie Yirsaw, Department of Health Promotion and Health Behavior, School of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Eyob Ketema Bogale, Health Promotion and Behavioral Science Department, School of Public Health, College of Medicine and Health Science, Bahir Dar University, Bahir Dar, Ethiopia.

Abiy Hailu Tikuneh, Department of Nutrition and Dietetics, Institute of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.

Abas Ali Hassen, Department of Anesthesia, Werabe University College of Medicine and Health Sciences, Werabe, Ethiopia.

Eyasu Alem Lake, School of Nursing, College of Health Science and Medicine, Wolaita Sodo University, Sodo, Ethiopia.

Tadesse Nigussie, Salale University College of Health Science Department of Public Health, Adama, Ethiopia.

Bushra Bibi, Office of Research Innovation and Commercialization, Khyber Medical University, Peshawar, Pakistan.

Fikiru Yigezu Jaleta, Department of Emergency and Critical Care Nursing, Menelik II Medical and Health Science College, Addis Ababa, Ethiopia.

Abebe Gedefaw Belete, Department of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia.

Genanew Kassie Getahun, Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia.

Author contributions

Kedir S Seid (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing [equal]), Chala G Jaleta (Conceptualization, Data curation, Formal analysis, Methodology, Software [equal]), Sintayehu S Lorato (Data curation, Methodology, Software, Visualization, Writing—review & editing [equal]), Mitiku Desalegn (Conceptualization, Investigation, Methodology, Software, Validation, Writing—review & editing [equal]), Dawit T Rundasa (Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing—review & editing [equal]), Yohannes Godie Ashebir (Conceptualization, Data curation, Formal analysis, Funding acquisition, Software, Visualization, Writing—review & editing [equal]), Olyad Kuma Getahun (Conceptualization, Data curation, Formal analysis, Software, Supervision, Visualization, Writing—review & editing [equal]), Gebeyehu Lakew (Conceptualization, Data curation, Formal analysis, Visualization, Writing—original draft, Writing—review & editing [equal]), Mathewos M Gemmechu (Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing—original draft, Writing—review & editing [equal]), Mulualem E Zeleke (Conceptualization, Data curation, Formal analysis, Software, Supervision, Validation, Writing—review & editing [equal]), Yibeltal A Atalay (Conceptualization, Data curation, Formal analysis, Investigation, Resources, Supervision, Writing—original draft, Writing—review & editing [equal]), Amlaku N Yirsaw (Conceptualization, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing [equal]), Eyob Ketema Bogale (Conceptualization, Formal analysis, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing [equal]), Abiy Hailu Tikuneh (Conceptualization, Formal analysis, Software, Supervision, Validation, Writing—original draft, Writing—review & editing [equal]), Abas A Hassen (Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing—review & editing [equal]), Eyasu A Lake (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Visualization, Writing—original draft, Writing—review & editing [equal]), Tadesse Nigussie (Data curation, Formal analysis, Investigation, Methodology, Resources, Writing—original draft, Writing—review & editing [equal]), Bushra Bibi (Data curation, Methodology, Software, Supervision, Visualization, Writing—original draft, Writing—review & editing [equal]), Fikru Yigezu Jaleta (Methodology, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing [equal]), Abebe Gedefaw Belete (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing [equal]), and Genanew K Getahun (Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing [equal])

Funding sources

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Conflicts of interest

The authors declare no conflicts of interest.

Data availability

All of the data are available in the manuscript.

Ethics statement

Not applicable.

Patient consent

Not applicable.

References

  • 1. Ezzedine  K, Eleftheriadou  V, Whitton  M, Van Geel  N. Vitiligo. Lancet  2015; 15:6736. [DOI] [PubMed] [Google Scholar]
  • 2. Mino  AE, Kelbore  AG. Prevalence of vitiligo and associated factors among adult patients attending Ayder Referral Teaching Hospital Dermatology Clinic in Mekelle Town, Tigray Region-Northern Ethiopia. J Health Med Nurs  2018; 52:69–78. [Google Scholar]
  • 3. Tsadik  AG, Teklemedhin  MZ, Mehari Atey  T  et al.  Public knowledge and attitudes towards vitiligo: a survey in Mekelle City, Northern Ethiopia. Dermatol Res Pract  2020; 2020:3495165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Sawant  NS, Vanjari  NA, Khopkar  U. Gender differences in depression, coping, stigma, and quality of life in patients of vitiligo. Dermatol Res Pract  2019; 2019:6879412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Picardo  M, Taïeb  A. Vitiligo. Rome: Springer, 2019 [Google Scholar]
  • 6. Alikhan  A, Felsten  LM, Daly  M, Petronic-Rosic  V. Vitiligo: a comprehensive overview: part I. Introduction, epidemiology, quality of life, diagnosis, differential diagnosis, associations, histopathology, etiology, and work-up. J Am Acad Dermatol  2011; 65:473–91. [DOI] [PubMed] [Google Scholar]
  • 7. Wang  Y, Li  S, Li  C. Perspectives of new advances in the pathogenesis of vitiligo: from oxidative stress to autoimmunity. Med Sci Monit  2019; 25:1017–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Glassman  SJ. Vitiligo, reactive oxygen species and T-cells. Clin Sci  2011; 120:99–120. [DOI] [PubMed] [Google Scholar]
  • 9. Di Bartolomeo  L, Custurone  P, Irrera  N  et al.  Vitiligo and mental health: natural compounds’ usefulness. Antioxidants  2023; 12:176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Alshiyab  DM, Al-Qarqaz  FA, Heis  LH  et al.  Assessment of serum vitamin D levels in patients with vitiligo in Jordan: a case–­control study. Dermatol Res Pract  2019; 2019:2048409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Dillon  AB, Sideris  A, Hadi  A, Elbuluk  N. Advances in vitiligo: an update on medical and surgical treatments. J Clin Aesthet Dermatol  2017; 10:15. [PMC free article] [PubMed] [Google Scholar]
  • 12. Tsadik  AG, Legesse  GF, Desta  DM  et al.  Clinico-epidemiological profile and treatment pattern of vitiligo in selected dermatological clinics of Mekelle City, Northern Ethiopia. Dermatol Res Pract  2020; 2020:3625753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Njoo  MD, Westerhof  W. Vitiligo: pathogenesis and treatment. Am J Clin Dermatol  2001; 2:167–81. [DOI] [PubMed] [Google Scholar]
  • 14. Khoury  M, Dabit  T, Siniora  H  et al.  The prevalence of vitiligo and its associated risk factors in the Middle East and Africa: a systematic review and meta-analysis. Health Sci Rev  2024; 12:100187. [Google Scholar]
  • 15. Esfandiarpour  I, Farajzadeh  S. Clinical characteristics of late-­onset vitiligo in an Iranian population. Dermatol Sin  2012; 30:43–6. [Google Scholar]
  • 16. Eram  U. Review article on beliefs and myths of vitiligo. Int J Eng Technol Sci Res  2017; 4:215–18. [Google Scholar]
  • 17. Rezaei  N, Gavalas  N, Weetman  A, Kemp  E. Autoimmunity as an aetiological factor in vitiligo. J Eur Acad Dermatol Venereol  2007; 21:865–76. [DOI] [PubMed] [Google Scholar]
  • 18. Alabi  G. Trends in the pattern of skin diseases in Nigeria. Niger Med J  1980; 10:163–8. [Google Scholar]
  • 19. Boza  JC, Kundu  RV, Fabbrin  A  et al.  Translation, cross-cultural adaptation and validation of the vitiligo-specific health-­related quality of life instrument (VitiQoL) into Brazilian Portuguese. An Bras Dermatol  2015; 90:358–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Al Robaee  AA. Assessment of quality of life in Saudi patients with vitiligo in a medical school in Qassim province, Saudi Arabia. Saudi Med J  2007; 28:1414–17. [PubMed] [Google Scholar]
  • 21. Ramakrishna  P, Rajni  T. Psychiatric morbidity and quality of life in vitiligo patients. Indian J Psychol Med  2014; 36:302–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Page  MJ, McKenzie  JE, Bossuyt  PM  et al.  The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ  2021; 372:n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Wells  G, Shea  B, O’Connell  D  et al. The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Available at: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (last accessed 20 June 2025)
  • 24. Khambalia  A, Seen  L. Trends in overweight and obese adults in Malaysia (1996–2009): a systematic review. Obes Rev  2010; 11:403–12. [DOI] [PubMed] [Google Scholar]
  • 25. DerSimonian  R, Laird  N. Meta-analysis in clinical trials. Control Clin Trials  1986; 7:177–88. [DOI] [PubMed] [Google Scholar]
  • 26. Mantel  N, Haenszel  W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst  1959; 22:719–48. [PubMed] [Google Scholar]
  • 27. Komba  EV, Mgonda  YM. The spectrum of dermatological disorders among primary school children in Dar es Salaam. BMC Public Health  2010; 10:765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Gashaw  Z, Shibeshi  D, Muhe  LM. Pattern of skin diseases in children attending the dermatology clinic in Alert referral hospital, Addis Ababa, Ethiopia: a retrospective study. Ethiop J Pediatr Child Health  2022; 17:43–55. [Google Scholar]
  • 29. Mamisoa  RI, Arilala  S, Judicaël  H  et al.  Childhood vitiligo seen in dermatology department of the university hospital Joseph Raseta Befelatanana, Antananarivo, Madagascar. EC Microbiol  2019; 15:334–8. [Google Scholar]
  • 30. Mamo  A, Enbiale  W, Shibabaw  T. Frequency of skin diseases and associated factors in Addis Alem Primary Hospital from June 2020–May 2021, Bahir Dar, Ethiopia: a cross-sectional study. Int J Cutan Disord Med  2024; 5:1–10. [Google Scholar]
  • 31. Mundeli  SP, Onguti  AG, Kizito  MS  et al.  Prevalence of vitiligo among patients attending the skin clinic at Mbarara Regional Referral Hospital, southwestern Uganda: a retrospective review. JAAD Int  2025; 20:21–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Kiprono  SK, Muchunu  JW, Masenga  JE. Skin diseases in pediatric patients attending a tertiary dermatology hospital in Northern Tanzania: a cross-sectional study. BMC Dermatol  2015; 15:16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Kibret  G, Haile  AT, Abebe  Y  et al.  Proportion of vitiligo and associated factors among patients visiting dermatology opd in tibebe ghion specialized hospital and addisalem primary hospital, bahirdar city, Ethiopia, 2023. BMC Res Notes  2024; 17:274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Ferié  J, Hatz  C. Skin disorders among schoolchildren in rural Tanzania and an assessment of contributing factors and therapeutic needs. Trop Doct  2006; 36:219–21. [DOI] [PubMed] [Google Scholar]
  • 35. Chhaganlal  K, Van Jaarsveld  I, Hoffmann  K  et al.  Cutaneous disorders in the “bairro Inhamudima” of Beira, Mozambique. Int J Dermatol  2007; 46:35–8. [DOI] [PubMed] [Google Scholar]
  • 36. Krüger  C, Schallreuter  KU. A review of the worldwide prevalence of vitiligo in children/adolescents and adults. Int J Dermatol  2012; 51:1206–12. [DOI] [PubMed] [Google Scholar]
  • 37. Wang  X, Du  J, Wang  T  et al.  Prevalence and clinical profile of vitiligo in China: a community-based study in six cities. Acta Dermatovenereol  2013; 93:62–5.38. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

vzag069_Supplementary_Data
vzag069_supplementary_data.docx (24.7KB, docx)

Data Availability Statement

All of the data are available in the manuscript.

Articles from Skin Health and Disease are provided here courtesy of Oxford University Press

RESOURCES